The final step in AI-driven Big Data processing is deploying trained models into production environments, where they can generate real-world predictions and insights. Model deployment involves integrating AI models with applications, APIs, or dashboards to deliver results to end-users. Tools like Docker and Kubernetes are commonly used to containerize and orchestrate AI models, ensuring scalability, reliability, and ease of deployment.

Monitoring deployed models is crucial to maintaining their performance over time. AI systems are susceptible to model drift, where changes in data distribution can degrade model accuracy. To address this, AI-driven monitoring tools track key performance metrics, detect anomalies, and trigger retraining processes when necessary. MLOps (Machine Learning Operations) practices streamline the lifecycle management of AI models, from development and deployment to monitoring and continuous improvement. This approach ensures that AI models remain accurate, efficient, and aligned with evolving business needs.